ericgillelandcsu.r-universe.dev

[ericgillelandcsu] verification 1.45

eric.gilleland@colostate.edu (Eric Gilleland) — Thu, 21 Aug 2025 13:30:02 GMT

Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.

[ericgillelandcsu] ismev 1.43

eric.gilleland@colostate.edu (Eric Gilleland) — Wed, 20 Aug 2025 17:00:15 GMT

Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.

[ericgillelandcsu] extRemes 2.2-1

eric.gilleland@colostate.edu (Eric Gilleland) — Fri, 16 May 2025 19:00:02 GMT

General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) and for bootstrapping, please see Gilleland (2020) .

[ericgillelandcsu] distillery 1.2-2

eric.gilleland@colostate.edu (Eric Gilleland) — Thu, 28 Nov 2024 23:40:06 GMT

Some very simple method functions for confidence interval calculation, bootstrap resampling aimed at atmospheric science applications, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx. To reference this package and for a tutorial on the bootstrap functions, please see Gilleland (2020) and Gilleland (2020) .

[ericgillelandcsu] smoothie 1.0-4

eric.gilleland@colostate.edu (Eric Gilleland) — Thu, 28 Nov 2024 23:40:02 GMT

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, ).

[ericgillelandcsu] SpatialVx 1.0-3

eric.gilleland@colostate.edu (Eric Gilleland) — Tue, 26 Nov 2024 23:00:17 GMT

Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) and for a tutorial on some of the main functions available here, see Gilleland (2022) .